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Mancini, T.; Mari, F.; Massini, A.; Melatti, I.; Tronci, E. |
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SyLVaaS: System Level Formal Verification as a Service |
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2016 |
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Fundamenta Informaticae |
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149 |
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1-2 |
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101-132 |
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MCLab @ davi @ DBLP:journals/fuin/ManciniMMMT16 |
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160 |
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Mancini, T.; Mari, F.; Melatti, I.; Salvo, I.; Tronci, E.; Gruber, J.; Hayes, B.; Prodanovic, M.; Elmegaard, L. |
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Parallel Statistical Model Checking for Safety Verification in Smart Grids |
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2018 |
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2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) |
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MCLab @ davi @ mancini-etal:2018:smartgridcomm |
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170 |
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Mancini, Toni; Mari, Federico; Massini, Annalisa; Melatti, Igor; Tronci, Enrico |
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SyLVaaS: System Level Formal Verification as a Service |
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Conference Article |
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2015 |
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Proceedings of the 23rd Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP 2015), special session on Formal Approaches to Parallel and Distributed Systems (4PAD) |
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MCLab @ davi @ |
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123 |
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Mancini, T.; Mari, F.; Massini, A.; Melatti, I.; Tronci, E. |
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Title |
On Checking Equivalence of Simulation Scripts |
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2021 |
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Journal of Logical and Algebraic Methods in Programming |
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100640 |
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Formal verification, Simulation based formal verification, Formal Verification of cyber-physical systems, System-level formal verification |
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To support Model Based Design of Cyber-Physical Systems (CPSs) many simulation based approaches to System Level Formal Verification (SLFV) have been devised. Basically, these are Bounded Model Checking approaches (since simulation horizon is of course bounded) relying on simulators to compute the system dynamics and thereby verify the given system properties. The main obstacle to simulation based SLFV is the large number of simulation scenarios to be considered and thus the huge amount of simulation time needed to complete the verification task. To save on computation time, simulation based SLFV approaches exploit the capability of simulators to save and restore simulation states. Essentially, such a time saving is obtained by optimising the simulation script defining the simulation activity needed to carry out the verification task. Although such approaches aim to (bounded) formal verification, as a matter of fact, the proof of correctness of the methods to optimise simulation scripts basically relies on an intuitive semantics for simulation scripting languages. This hampers the possibility of formally showing that the optimisations introduced to speed up the simulation activity do not actually omit checking of relevant behaviours for the system under verification. The aim of this paper is to fill the above gap by presenting an operational semantics for simulation scripting languages and by proving soundness and completeness properties for it. This, in turn, enables formal proofs of equivalence between unoptimised and optimised simulation scripts. |
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2352-2208 |
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MCLab @ davi @ Mancini2021100640 |
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183 |
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Cavaliere, Federico; Mari, Federico; Melatti, Igor; Minei, Giovanni; Salvo, Ivano; Tronci, Enrico; Verzino, Giovanni; Yushtein, Yuri |
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Model Checking Satellite Operational Procedures |
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2011 |
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DAta Systems In Aerospace (DASIA), Org. EuroSpace, Canadian Space Agency, CNES, ESA, EUMETSAT. San Anton, Malta, EuroSpace. |
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We present a model checking approach for the automatic verification of satellite operational procedures (OPs). Building a model for a complex system as a satellite is a hard task. We overcome this obstruction by using a suitable simulator (SIMSAT) for the satellite. Our approach aims at improving OP quality assurance by automatic exhaustive exploration of all possible simulation scenarios. Moreover, our solution decreases OP verification costs by using a model checker (CMurphi) to automatically drive the simulator. We model OPs as user-executed programs observing the simulator telemetries and sending telecommands to the simulator. In order to assess feasibility of our approach we present experimental results on a simple meaningful scenario. Our results show that we can save up to 90% of verification time. |
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yes |
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Sapienza @ mari @ Dasia11 |
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13 |
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Melatti, I.; Mari, F.; Mancini, T.; Prodanovic, M.; Tronci, E. |
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Title |
A Two-Layer Near-Optimal Strategy for Substation Constraint Management via Home Batteries |
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Journal Article |
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2021 |
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IEEE Transactions on Industrial Electronics |
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1-1 |
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Within electrical distribution networks, substation constraints management requires that aggregated power demand from residential users is kept within suitable bounds. Efficiency of substation constraints management can be measured as the reduction of constraints violations w.r.t. unmanaged demand. Home batteries hold the promise of enabling efficient and user-oblivious substation constraints management. Centralized control of home batteries would achieve optimal efficiency. However, it is hardly acceptable by users, since service providers (e.g., utilities or aggregators) would directly control batteries at user premises. Unfortunately, devising efficient hierarchical control strategies, thus overcoming the above problem, is far from easy. We present a novel two-layer control strategy for home batteries that avoids direct control of home devices by the service provider and at the same time yields near-optimal substation constraints management efficiency. Our simulation results on field data from 62 households in Denmark show that the substation constraints management efficiency achieved with our approach is at least 82% of the one obtained with a theoretical optimal centralized strategy. |
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To appear |
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MCLab @ davi @ ref9513535 |
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190 |
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Mancini, T.; Melatti, I.; Tronci, E. |
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Any-horizon uniform random sampling and enumeration of constrained scenarios for simulation-based formal verification |
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2021 |
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IEEE Transactions on Software Engineering |
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1-1 |
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Model-based approaches to the verification of non-terminating Cyber-Physical Systems (CPSs) usually rely on numerical simulation of the System Under Verification (SUV) model under input scenarios of possibly varying duration, chosen among those satisfying given constraints. Such constraints typically stem from requirements (or assumptions) on the SUV inputs and its operational environment as well as from the enforcement of additional conditions aiming at, e.g., prioritising the (often extremely long) verification activity, by, e.g., focusing on scenarios explicitly exercising selected requirements, or avoiding </i>vacuity</i> in their satisfaction. In this setting, the possibility to efficiently sample at random (with a known distribution, e.g., uniformly) within, or to efficiently enumerate (possibly in a uniformly random order) scenarios among those satisfying all the given constraints is a key enabler for the practical viability of the verification process, e.g., via simulation-based statistical model checking. Unfortunately, in case of non-trivial combinations of constraints, iterative approaches like Markovian random walks in the space of sequences of inputs in general fail in extracting scenarios according to a given distribution (e.g., uniformly), and can be very inefficient to produce at all scenarios that are both legal (with respect to SUV assumptions) and of interest (with respect to the additional constraints). For example, in our case studies, up to 91% of the scenarios generated using such iterative approaches would need to be neglected. In this article, we show how, given a set of constraints on the input scenarios succinctly defined by multiple finite memory monitors, a data structure (scenario generator) can be synthesised, from which any-horizon scenarios satisfying the input constraints can be efficiently extracted by (possibly uniform) random sampling or (randomised) enumeration. Our approach enables seamless support to virtually all simulation-based approaches to CPS verification, ranging from simple random testing to statistical model checking and formal (i.e., exhaustive) verification, when a suitable bound on the horizon or an iterative horizon enlargement strategy is defined, as in the spirit of bounded model checking. |
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1939-3520 |
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MCLab @ davi @ ref9527998 |
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191 |
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Alimguzhin, V.; Mari, F.; Melatti, I.; Tronci, E.; Ebeid, E.; Mikkelsen, S.A.; Jacobsen, R.H.; Gruber, J.K.; Hayes, B.; Huerta, F.; Prodanovic, M. |
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A Glimpse of SmartHG Project Test-bed and Communication Infrastructure |
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2015 |
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Digital System Design (DSD), 2015 Euromicro Conference on |
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225-232 |
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Batteries; Control systems; Databases; Production; Sensors; Servers; Smart grids; Grid State Estimation; Peak Shaving; Policy Robustness Verification; Price Policy Synthesis |
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Sapienza @ preissler @ Alimguzhin_etal2015 |
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Mancini, Toni; Mari, Federico; Massini, Annalisa; Melatti, Igor; Tronci, Enrico |
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Simulator Semantics for System Level Formal Verification |
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2015 |
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Proceedings Sixth International Symposium on Games, Automata, Logics and Formal Verification (GandALF 2015), |
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MCLab @ davi @ |
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125 |
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Toni Mancini; Enrico Tronci; Ivano Salvo; Federico Mari; Annalisa Massini; Igor Melatti |
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Computing Biological Model Parameters by Parallel Statistical Model Checking |
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2015 |
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International Work Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2015) |
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9044 |
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542-554 |
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MCLab @ davi @ |
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124 |
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